System, method and computer program product for measuring blood properties form a spectral image

ABSTRACT

A system, method and computer program product is provided for analyzing spectral images of a micrcirculatory system to measure the volume and concentration of a blood vessel. The images are analyzed to identify vessel structure, measure the light absorption and develop a contrast gradient plus (KGP) estimate. The KGP estimate is used to predict blood characteristics, such as hemoglobin concentration and hematocrit. The KGP is estimated in three distinct phases. First, the images are screened to measure a mean image intensity and motion blur. Second, each image is analyzed to identify background curvate, and create vessel, background and diameter masks. To identify background curvate, the images are analyzed to detect shadows caused by larger blood vessels in the background image. The diameter and area of the vessels are also calculated. During the Prediction and Calibration phase, the images are screened to eliminate all images failing thresholds for mean intensity, motion blur, background curvature and area-to-perimeter ratio. Finally, the KGP estimate is determined from the selected images.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates generally to reflected lightanalysis. More particularly, the invention relates to the use ofreflected spectral imaging to analyze visualizable components of a fluidflowing in a tubular system. Still more particularly, the inventionrelates to the use of reflected spectral imaging to analyze thecomponents of blood in a mammalian, especially human, vascular system.

[0003] 2. Related Art

[0004] Widely accepted medical school doctrine teaches that the completeblood count including the white blood cell differential (CBC+Diff) isone of the best tests to assess a patient's overall health. With it, aphysician can detect or diagnose anemia, infection, blood loss, acuteand chronic diseases, allergies, and other conditions. CBC+Diff analysesprovide comprehensive information on constituents in blood, includingthe number of red cells, the hematocrit, the hemoglobin concentration,and indices that portray the size, shape, and oxygen-carryingcharacteristics of the entire red blood cell (RBC) population. TheCBC+Diff also includes the number and types of white blood cells and thenumber of platelets. The CBC+Diff is one of the most frequentlyrequested diagnostic tests with about two billion done in the UnitedStates per year.

[0005] A conventional CBC+Diff test is done in an “invasive” manner inwhich a sample of venous blood is drawn from a patient through a needle,and submitted to a laboratory for analysis. For example, a phlebotomist(an individual specially trained in drawing blood) collects a sample ofvenous blood into a tube containing an anticoagulant to prevent theblood from clotting. The sample is then sent to a hematology laboratoryto be processed, typically on automated, multiparameter analyticalinstruments, such as those manufactured by Coulter Diagnostics of Miami,Fla. The CBC+Diff test results are returned to the requesting physician,typically on the next day.

[0006] In medical diagnosis it is often necessary to measure other typesof blood components, such as non-cellular constituents present in theplasma component of blood. Such constituents can include, for example,blood gases and bilirubin. Bilirubin is a reddish to yellow pigmentproduced in the metabolic breakdown of hemoglobin and other proteins.Bilirubin is removed from the blood by the liver and is excreted fromthe body. However, the livers of newborn children, especially prematurebabies, cannot process bilirubin effectively.

[0007] The birth process often results in extensive bruising, resultingin blood escaping into the tissues where it is broken downmetabolically. For this and other medical causes, bilirubin mayaccumulate in the blood stream. If bilirubin levels rise high enough, itbegins to be deposited in other body tissues causing jaundice. Its firstappearance is in the eye. At still higher levels, deposition begins indeeper tissues, including the brain, and can result in permanent braindamage.

[0008] The most common method for bilirubin analysis is through an invitro process. In such an in vitro process, a blood sample is invasivelydrawn from the patient. The formed elements (red blood cells and othercells) are separated by centrifugation and the remaining fluid isreacted chemically and analyzed spectrophotometrically.

[0009] Invasive techniques, such as for conventional CBC+Diff tests andbilirubin analysis, pose particular problems for newborns because theircirculatory system is not yet fully developed. Blood is typically drawnusing a “heel stick” procedure wherein one or more punctures are made inthe heel of the newborn, and blood is repeatedly squeezed out into acollecting tube. This procedure is traumatic even for an infant in goodhealth. More importantly, this procedure poses the risk of having to doa blood transfusion because of the low total blood volume of the infant.The total blood volume of the newborn infant is 60-70 cc/kg body weight.Thus, the total blood volume of low birth weight infants (under 2500grams) cared for in newborn intensive care units ranges from 45-175 cc.Because of their low blood volume and delay in production of red cellsafter birth, blood sampling from preterm infants and other sick infantsfrequently necessitates transfusions for these infants. Blood bank usefor transfusion of infants in neonatal intensive care units is secondonly to the usage for cardiothoracic surgery. In addition to newborns,invasive techniques are also particularly stressful for, and/ordifficult to carryout on, children, elderly patients, bum patients, andpatients in special care units.

[0010] A hierarchical relationship exists between the laboratoryfindings and those obtained at the physical examination. The demarcationbetween the physical findings of the patient and the laboratory findingsare, in general, the result of technical limitations. For instance, inthe diagnosis of anemia (defined as low hemoglobin concentration), it isfrequently necessary to quantify the hemoglobin concentration or thehematocrit in order to verify the observation of pallor. Pallor is thelack of the pink color of skin which frequently signals the absence orreduced concentration of the heavily red pigmented hemoglobin. However,there are some instances in which pallor may result from other causes,such as constriction of peripheral vessels, or being hidden by skinpigmentation. Because certain parts of the integument are less affectedby these factors, clinicians have found that the pallor associated withanemia can more accurately be detected in the mucous membrane of themouth, the conjunctivae, the lips, and the nail beds. A device which isable to rapidly and non-invasively quantitatively determine thehemoglobin concentration directly from an examination of one or more ofthe foregoing areas would eliminate the need to draw a venous bloodsample to ascertain anemia. Such a device would also eliminate the delayin waiting for the laboratory results in the evaluation of the patient.Such a device also has the advantage of added patient comfort.

[0011] Soft tissue, such as mucosal membranes or unpigmented skin, donot absorb light in the visible and near-infrared, i.e., they do notabsorb light in the spectral region where hemoglobin absorbs light. Thisallows the vascularization to be differentiated by spectral absorptionfrom surrounding soft tissue background. However, the surface of softtissue strongly reflects light and the soft tissue itself effectivelyscatters light after penetration of only 100 microns. Therefore, in vivovisualization of the circulation is difficult because of poorresolution, and generally impractical because of the complexitiesinvolved in compensating for multiple scattering and for specularreflection from the surface. Studies on the visualization of cells inthe microcirculation consequently have been almost exclusively invasive,using a thin section (less than the distance for multiple scattering) oftissue containing the microcirculation, such as the mesentery, that canbe observed by a microscope using light transmitted through the tissuesection. Other studies have experimented with producing images oftissues from within the multiple scattering region by time gating (see,Yodh, A. and B. Chance, Physics Today, March, 1995, 34-40). However, theresolution of such images is limited because of the scattering of light,and the computations for the scattering factor are complex.

[0012] Spectrophotometry involves analysis based on the absorption orattenuation of electromagnetic radiation by matter at one or morewavelengths of light. The instruments used in this analysis are referredto as spectrophotometers. A simple spectrophotometer includes: a sourceof radiation, such as, e.g., a light bulb; a spectral selection means,such as a monochromator containing a prism or grating or colored filter;and one or more detectors, such as, e.g., photocells, which measure theamount of light transmitted and/or reflected by the sample in theselected spectral region.

[0013] In opaque samples, such as solids or highly absorbing solutions,the radiation reflected from the surface of the sample maybe measuredand compared with the radiation reflected from a non-absorbing or whitesample. If this reflectance intensity is plotted as a function ofwavelength, it gives a reflectance spectrum: Reflectance spectra arecommonly used in matching colors of dyed fabrics or painted surfaces.However, because of its limited range and inaccuracy, reflectionspectrophotometry has been used primarily in qualitative rather thanquantitative analysis. On the other hand, transmission spectrophotometryis conventionally used for quantitative analysis because Beer's law(inversely relating the logarithm of measured intensity linearly toconcentration) can be used.

[0014] Reflective spectrophotometry is conventionally avoided forquantitative analysis because specularly reflected light from a surfacelimits the available contrast (black to white or signal to noise ratio),and, consequently, the measurement range and linearity. Because ofsurface effects, measurements are usually made at an angle to thesurface. However, only for the special case of a Lambertian surface willthe reflected intensity be independent of the angle of viewing. Lightreflected from a Lambertian surface appears equally bright in alldirections (cosine law). However, good Lambertian surfaces are difficultto obtain. Conventional reflection spectrophotometry presents an evenmore complicated relationship between reflected light intensity andconcentration than exists for transmission spectrophotometry whichfollows Beer's law. Under the Kubelka-Munk theory applicable inreflection spectrophotometry, the intensity of reflected light can berelated indirectly to concentration through the ratio of absorption toscattering.

[0015] Some imaging studies have been done in the reflected light of themicrocirculation of the nail beds on patients with Raynauds, diabetes,and sickle cell disease. These studies were done to obtain experimentaldata regarding capillary density, capillary shape, and blood flowvelocity, and were limited to gross physical measurements oncapillaries. No spectral measurements, or individual cellularmeasurements, were made, and Doppler techniques were used to assessvelocity. The non-invasive procedure employed in these studies could beapplied to most patients, and in a comfortable manner.

[0016] One non-invasive device for iii vivo analysis is disclosed inU.S. Pat. No. 4,998,533 to Winkei-nan. The Winkelman device uses imageanalysis and reflectance spectrophotometry to measure individual cellparameters such as cell size. Measurements are taken only within smallvessels, such as capillaries where individual cells can be visualized.Because the Winkelman device takes measurements only in capillaries,measurements made by the Winkelman device will not accurately reflectmeasurements for larger vessels. This inaccuracy results from theconstantly changing relationship of volume of cells to volume of bloodin small capillaries resulting from the non-Newtonian viscositycharacteristic of blood. Consequently, the Winkelman device is notcapable of measuring the central or true hematocrit, or the totalhemoglobin concentration, which depend upon the ratio of the volume ofred blood cells to that of the whole blood in a large vessel such as avein.

[0017] The Winkelman device measures the number of white blood cellsrelative to the number of red blood cells by counting individual cellsas they flow through a micro-capillary. The Winkelman device dependsupon accumulating a statistically reliable number of white blood cellsin order to estimate the concentration. However, blood flowing through amicro-capillary will contain approximately 1000 red cells for everywhite cell, making this an impractical method. The Winkelman device doesnot provide any means by which platelets can be visualized and counted.Further, the Winkelman device does not provide any means by which thecapillary plasma can be visualized, or the constituents of the capillaryplasma quantified. The Winkelman device also does not provide a means bywhich abnormal constituents of blood, such as tumor cells, can bedetected.

[0018] Another non-invasive device for in vivo analysis is disclosed incommonly assigned U.S. Pat. No. 5,983,120, issued Nov. 9, 1999, in thenames of Warren Groner and Richard G. Nadeau, and entitled “Method andApparatus for Reflected Imaging Analysis” (hereinafter referred to as“the '120 patent”), or in commonly assigned U.S. Pat. No. 6,104,939,issued Aug. 15, 2000, in the names of Warren Groner and Richard G.Nadeau, and entitled “Method and Apparatus for Reflected ImagingAnalysis” (hereinafter referred to as “the '939 patent”). The disclosureof the '120 patent and the '939 patent are incorporated herein byreference as though set forth in its entirety. The device of the '120patent or the '939 patent provides for complete non-invasive in vivoanalysis of a vascular system. This device provides for high resolutionvisualization of blood cell components (red blood cells, white bloodcells, and platelets), blood rheology, blood vessels, andvascularization throughout the vascular system. The device of the '120patent or the '939 patent allows quantitative determinations to be madefor blood cells, normal and abnormal contents of blood cells, as well asfor normal and abnormal constituents of blood plasma.

[0019] The device of the '120 patent or the '939 patent captures a rawreflected image of a blood sample, and normalizes the image with respectto the background to form a corrected reflected image. An analysis imageis segmented from the corrected reflected image to include a scene ofinterest for analysis. The method and apparatus disclosed in the '120patent or the '939 patent can be used to determine such characteristicsas the hemoglobin concentration per unit volume of blood, the number ofwhite blood cells per unit volume of blood, a mean cell volume, thenumber of platelets per unit volume of blood, and the hematocrit.

[0020] To accurately determine the blood characteristics, however, theimages need to be screened to identify images having good measurableproperties. The measurements taken from the images also need to bescreened, normalized and corrected to obtain better estimates of thetrue value of the blood characteristics.

[0021] Thus, there is a need in the art for a method and device thatselects images having good measurable properties and provides reliable,quantitative estimates of blood cells, normal and abnormal contents ofblood cells, and normal and abnormal constituents of blood plasma byusing non-invasive in vivo analysis.

SUMMARY OF THE INVENTION

[0022] The present invention is directed to processing reflectedspectral images of a microcirculatory system to measure the volume andconcentration of a blood vessel, including arteries, veins andcapillaries. Basically, the method and apparatus of the presentinvention analyze the spectral image (also referred to herein as “bloodsample” or “image”) to identify vessel structure and measure the lightabsorption in the vessel to develop what is referred to as a contrastgradient plus (KGP) estimate. The KGP estimate is used to measure bloodcharacteristics, such as the hemoglobin concentration and hematocrit ofthe blood sample.

[0023] The KGP estimate is calculated in three steps or phases:Screening, Analysis, and Calibration & Prediction. The images are firstscreened to measure mean image intensity and motion blur parameters. Itshould be noted that, in an embodiment, the screening phase is used onlyto detect certain parameters. No image is eliminated at this step in theprocess. However, in another embodiment, an optimal screening thresholdcan be implemented to reject images that do not meet the screeningthreshold at this step.

[0024] Next, the spectral image is analyzed to identify backgroundcurvature, and create vessel, background and diameter masks. First, toidentify background curvature, the images are analyzed to identifyshadows caused by larger blood vessels in the background of the smallerblood vessel(s) being evaluated. Afterwards, the image is processed tosegment all vascular structure from nonvascular regions. During thisprocess, a model of the vascular structure is developed to create avessel image. A model also is developed to create a background image.The diameter and area of the vessels are calculated, as well. It shouldbe noted that these steps may occur concurrently or in a differentorder.

[0025] Finally, the images enter a Prediction and Calibration phasewhere the images are subsequently screened to eliminate all images thatfail to pass certain thresholds for motion blur and background curvaturecriteria. Of the remaining images, the vessel area is used to detectanemia and select better images for anemic patients. At last, the KGPestimate is determined from the selected images.

[0026] The method of the present invention can be used to determinevarious characteristics of blood. Such characteristics can include thehemoglobin concentration per unit volume of blood, the number of whiteblood cells per unit volume of blood, a mean cell volume, a mean cellhemoglobin concentration, the number of platelets per unit volume ofblood, and the hematocrit.

[0027] The method is used to perform inZ vivo analysis of blood in largevessels, and in vivo analysis of blood in small vessels to determineblood parameters such as concentrations and blood cell counts. Themethod of the present invention can also be used to conduct non-invasivein vivo analysis of non-cellular characteristics of capillary plasma.The method of the present invention can also be used to perform in vitroanalyses by imaging blood in, for example, a tube or flow cell.

[0028] The method of the present invention can also be used to analyzeother types of fluids containing visualizable components. The reflectedspectral imaging system can be used to analyze fluids for particulateimpurities. It is only necessary that the walls of the fluid path besufficiently transparent to permit light to pass through the walls ofthe fluid path to image the fluid and any impurities flowing in thepath.

[0029] Features and Advantages

[0030] It is a feature of the present invention that it provides fornon-invasive in vivo analysis of the vascular system.

[0031] It is a further feature of the present invention thatquantitative analyses of both formed blood components (red blood cells,white blood cells, and platelets) and non-formed blood components, suchas capillary plasma, can be done.

[0032] It is yet a further feature of the present invention that perunit volume or concentration measurements, such as hemoglobin,hematocrit, and blood cell counts, can be made through the use ofreflected spectral images of the vascular system.

[0033] It is yet a further feature of the present invention that bloodcells, blood vessels, and capillary plasma can be visualized andsegmented into an analysis image.

[0034] A still farther feature of the present invention is that it canbe used to determine characteristics, such as the hemoglobinconcentration per unit volume of blood, the number of white blood cellsper unit volume of blood, the mean cell volume, the mean cell hemoglobinconcentration, the number of platelets per unit volume of blood, and thehematocrit through the use of reflected spectral imaging.

[0035] An advantage of the present invention is that it provides a meansfor the rapid, non-invasive measurement of clinically significantparameters of the CBC+Diff test. It advantageously provides immediateresults. As such, it can be used for point-of-care testing anddiagnosis.

[0036] A further advantage of the present invention is that iteliminates the invasive technique of drawing blood. This eliminates thepain and difficulty of drawing blood from newborns, children, elderlypatients, bum patients, and patients in special care units. The presentinvention is also advantageous in that it obviates the risk of exposureto AIDS, hepatitis, and other blood-borne diseases.

[0037] A still further advantage of the present invention is that itprovides for overall cost savings by eliminating sample transportation,handling, and disposal costs associated with conventional invasivetechniques.

[0038] A still further advantage of the present invention is that itprovides for substantially improved range and accuracy for reflectionspectrophotometry. The present invention is also advantageous in that itpermits use of a simple relationship between concentration andintensity.

[0039] A still further advantage of the present invention is that itprovides for improved visualization of reflected images of any object,and for quantitative and qualitative analyses of these reflected images.

BRIEF DESCRIPTION OF THE FIGURES

[0040] The present invention is described with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements. Additionally, the left-mostdigit(s) of a reference number identifies the drawing in which thereference number first appears.

[0041]FIG. 1 shows a flow chart representing the general operationalflow according to an embodiment of a method of the present invention;

[0042]FIG. 2 shows a flow chart illustrating step 150 shown in FIG. 1;

[0043]FIG. 3 shows a flow chart illustrating step 215 shown in FIG. 2;

[0044]FIG. 4 shows a flow chart representing the general operationalflow according to an embodiment of the motion detection method of thepresent invention;

[0045]FIG. 5A shows a flow chart representing the general operationalflow according to an embodiment for calculating parameters and imagesrequired for the diameter estimation method of the present invention;

[0046]FIG. 5B shows a flow chart representing the general operationalflow according to an embodiment of the diameter estimation method of thepresent invention;

[0047]FIG. 6 shows a flow chart illustrating step 210 shown in FIG. 2;

[0048]FIG. 7 shows a flow chart representing the general operationalflow according to an embodiment of the calibration and prediction methodof the present invention;

[0049]FIG. 8 is a block diagram of an example computer system useful forimplementing the present invention; and

[0050]FIG. 9 shows a flow chart representing the general operationalflow according to an embodiment of the area-to-perimeter estimationmethod of the present invention.

DETAILED DESCRIPTIOIZ OF THE PREFERRED EMBODINIENTS

[0051] I. Overview of the Present Invention

[0052] The present invention is directed to a method and apparatus foranalysis, particularly non-invasive, in vivo analysis of a subject'svascular system. The in vivo measurements discussed herein can also beperformed in vitro by imaging blood in, for example, a tube or flowcell, as would be apparent to a person skilled in the relevant art(s).The in vivo method is carried out by imaging a portion of the subject'svascular system. For example, the image can be created from asub-surface region of a subject's tissues or organs. The tissue coveringthe imaged portion must be traversed by light without multiplescattering to obtain a reflected image. In order to form an image, twocriteria must be met. First, there must be image contrast resulting froma difference in the optical properties, such as absorption, index ofrefraction, or scattering characteristics, between the subject to beimaged and its surroundings or background. Second, the light that iscollected from the subject must reach an image capturing means withoutsubstantial scattering, i.e., the reflected image must be captured froma depth that is less than the multiple scattering length. As usedherein, “image” refers to any image that satisfies the foregoing twocriteria. As used herein, “reflected image” refers to the image of asubject in reflected light. The resolution required for capturing theimage is dictated by the spatial homogeneity of the imaged portion. Forexample, a reflected image of individual cells requires high resolution.A reflected image of large vessels can be done with low resolution. Areflected image suitable for making a determination based on pallorrequires very low resolution.

[0053] The tissue covering the imaged portion is thus preferablytransparent to light, and relatively thin, such as the mucosal membraneon the inside of the lip of a human subject. As used herein, “light”refers generally to electromagnetic radiation of any wavelength,including the infrared, visible, and ultraviolet portions of thespectrum. A particularly preferred portion of the spectrum is thatportion where there is relative transparency of tissue, such as in thevisible and near-infrared wavelengths. It is to be understood that forthe present invention, light can be coherent light or incoherent light,and illumination may be steady or in pulses of light.

[0054] The reflected image is corrected to form a corrected reflectedimage. The correction to the reflected image is done, for example, toisolate particular wavelengths of interest, or to extract a movingportion of the image from a stationary portion of the image. A scene issegmented from the corrected reflected image to form an analysis image.The analysis image is then analyzed for the desired characteristic ofthe subject's vascular system.

[0055] The method of the present invention can be used for analysis inlarge and small vessels, including capillary plasma. As used herein,“large vessel” refers to a vessel in the vascular system of sufficientsize so that a plurality of red blood cells flow side-by-side throughit. “Small vessel” refers to a vessel in the vascular system of a sizeso that red blood cells flow substantially “single file” through it. Asexplained in more detail below, the present invention uses reflectance,not transmission, for the images that are analyzed. That is, the imageis made by “looking at” the vascular system, rather than by “lookingthrough” the vascular system. However, because of the features of theimaging system used in the present invention, as described in detail inthe above-referenced '120 patent or the '939 patent, the image appearsto be of the transmission type. For this reason, Beer's law can beapplied to quantitatively measure the images. Per unit volume orconcentration measurements can be made directly from the images.Therefore, although the present invention uses reflectance, it would beapparent to a person skilled in the relevant art(s) that the method ofthe present invention can be used on both transmitted and reflectedimages.

[0056] By using the method of the present invention to provide areflected spectral image of large vessels, the hemoglobin (Hb),hematocrit (Hct), and white blood cell count (WBC) parameters can bedirectly determined. By using the method of the present invention toprovide a reflected spectral image of small vessels, mean cell volume(MCV), mean cell hemoglobin concentration (MCHC), and platelet count(Plt) can be directly determined.

[0057] Human blood is made up of formed elements and plasma. There arethree basic types of formed blood cell components: red blood cells(erythrocytes); white blood cells (leukocytes); and platelets. As notedabove, red blood cells contain hemoglobin that carries oxygen from thelungs to the tissues of the body. White blood cells are of approximatelythe same size as red blood cells, but do not contain hemoglobin. Anormal healthy individual will have approximately 5,000,000 red bloodcells per cubic millimeter of blood, and approximately 7,500 white bloodcells per cubic millimeter of blood. Therefore, a normal healthyindividual will have approximately one white blood cell for every 670red blood cells circulating in the vascular system.

[0058] A complete blood count (CBC) without white blood celldifferential measures eight parameters: (1) hemoglobin (Hb); (2)hematocrit (Hct); (3) red blood cell count (RBC); (4) mean cell volume(MCV); (5) mean cell hemoglobin (MCH); (6) mean cell hemoglobinconcentration (MCHC); (7) white blood cell count (WBC); and (8) plateletcount (Plt). The first six parameters are referred to herein as RBCparameters. Concentration measurements (measurements per unit volume ofblood) are necessary for producing values for Hb, Hct, RBC, WBC, andPlt. Hb is the hemoglobin concentration per unit volume of blood. Hct isthe volume of cells per unit volume of blood. Hct can be expressed as apercentage, i.e.,:

(cell volume÷volume of blood)×100%  (Eqn. 1)

[0059] RBC is the number of red blood cells per unit volume of blood.WBC is the number of white blood cells per unit volume of blood. Plt isthe number of platelets per unit volume of blood.

[0060] Red cell indices (MCV, MCH, and MCHC) are cellular parametersthat depict the volume, hemoglobin content, and hemoglobinconcentration, respectively, of the average red cell. The red cellindices may be determined by making measurements on individual cells,and averaging the individual cell measurements. Red cells do not changevolume or lose hemoglobin as they move through the vascular system.Therefore, red cell indices are constant throughout the circulation, andcan be reliably measured in small vessels. The three red cell indicesare related by the equation:

MCHC=MCH÷MCV  (Eqn. 2)

[0061] Thus, only two red cell indices are independent variables.

[0062] To determine values for the six RBC parameters listed above, thefollowing two criteria must be met. First, three of the parameters mustbe independently measured or determined. That is, three of theparameters must be measured or determined without reference to any ofthe other of the six parameters. Second, at least one of the threeindependently measured or determined parameters must be a concentrationparameter (per unit volume of blood). Therefore, values for the six keyparameters can be determined by making three independent measurements,at least one of which is a concentration measurement which cannot bemade in a small vessel.

[0063] As disclosed in the '120 patent or the '939 patent, Hb and Hctcan be directly measured by reflected spectral imaging of large vessels,and MCV can be directly measured by reflected spectral imaging of smallvessels. In this manner, three parameters are independently measured,and two of the parameters (Hb and Hct) are concentration parametersmeasured per unit volume of blood. As such, the six RBC parameterslisted above can be determined in the following manner: Hb Directlymeasured Hct Directly measured RBC Hct ÷ MCV MCV Directly measured NCHMCV × (Hb ÷ Hct) MCHC Hb ÷ Hct

[0064] Also, as disclosed in the '120 patent or the '939 patent, Hb canbe directly measured by reflected spectral imaging of large vessels, andMCV and MCHC can be directly measured by reflected spectral imaging ofsmall vessels. In this manner, three parameters are independentlymeasured, and one of the parameters (Hb) is a concentration parametermeasured per unit volume of blood. As such, the six RBC parameterslisted above can be determined in the following manner: Hb Directlymeasured Hct Hb ÷ NCHC RBC Hb ÷ (MCV × MCHC) MCV Directly measured MCHMCV × MCHC MCHC Directly measured

[0065] Concentration measurements are measurements per unit volume. Asdiscussed above, a measurement made per unit area is proportional to ameasurement made per unit volume (volume measurement with constantdepth) when the depth of penetration is constant. The depth ofpenetration is a function of wavelength, the size of the particles withwhich it interacts, and refractive index. For blood, the particle sizeand index of refraction are essentially constant. Consequently, thedepth of penetration will be constant for a particular wavelength.

[0066] Hemoglobin is the main component of red blood cells. Hemoglobinis a protein that serves as a vehicle for the transportation of oxygenand carbon dioxide throughout the vascular system. Hemoglobin absorbslight at particular absorbing wavelengths, such as 550 nm, and does notabsorb light at other non-absorbing wavelengths, such as 650 nm. UnderBeer's law, the negative logarithm of the measured transmitted lightintensity is linearly related to concentration. As explained more fullyin the '120 patent or the '939 patent, a spectral imaging apparatus canbe configured so that reflected light intensity follows Beer's law.Assuming Beer's law applies, the concentration of hemoglobin in aparticular sample of blood is linearly related to the negative logarithmof light reflected by the hemoglobin. The more 550 nm light absorbed bya blood sample, the lower the reflected light intensity at 550 nm, andthe higher the concentration of hemoglobin in that blood sample. Theconcentration of hemoglobin can be computed by taking the negativelogarithm of the measured reflected light intensity at an absorbingwavelength such as 550 nm. Therefore, if the reflected light intensityfrom a particular sample of blood is measured, the concentration in theblood of such components as hemoglobin can be directly determined.

[0067] The method of the present invention can also be used to determinethe hematocrit (Hct). The difference between hemoglobin (which is thegrams of hemoglobin per volume of blood) and hematocrit (which is thevolume of blood cells per volume of blood) is determined by theconcentration of hemoglobin within the cells which determines the indexof refraction of the cells. Hence, measurements in which the imagecontrast between the circulation and the background is achievedprincipally by the scattering properties of the circulation will berelated to the hematocrit and those obtained principally by theabsorbing properties will be related primarily to the hemoglobin. Forexample, the microvascular system beneath the mucosal membrane on theinside of the lip of a human subject can be imaged to produce a rawreflected image whose contrast is determined by a difference in thescattering properties of the blood cells.

[0068] As disclosed in the '120 patent or the '939 patent, a spectralimaging apparatus includes a light source that is used to illuminate theportion of the subject's vascular system to be imaged. The reflectedlight is captured by an image capturing means. Suitable image capturingmeans include, but are not limited to, a camera, a film medium, aphotocell, a photodiode, or a charge coupled device camera. An imagecorrecting and analyzing means, such as a computer, is coupled to theimage capturing means for carrying out image correction, scenesegmentation, and blood characteristic analysis.

[0069] To implement the method of the present invention, the reflectedimage is captured and processed to select images having good measurableproperty. The selected image(s) is screened and analyzed to produce areliable measurement of the concentration and volume of bloodcharacteristics, such as hematocrit or hemoglobin. FIG. 1 illustrates ageneral operational flow of one embodiment of the present invention.More specifically, flowchart 100 shows an example of a process foranalyzing a spectral image of a blood or tissue sample. The spectralimage can be obtained from a spectral imaging apparatus preferably, butnot necessarily, of the type described in the '120 patent or the '939patent. Nonetheless, the spectral image can be obtained from any type ofimaging apparatus designed for tissue or blood analysis, as would beapparent to a person skilled in the relevant art(s).

[0070]FIG. 1 starts at step 101 and passes immediately to step 105,where the spectral images are retrieved from a memory source or imagedirectory. The images can be retrieved from an input file stored in atemporary or permanent memory location on a hard disk drive or removablestorage device, such as a floppy diskette, magnetic tape, optical disks,etc., or the like, as would be apparent to a person skilled in therelevant art(s). The input file also includes the subject number orother data used to identify the patient. At step 105, an output file isalso created to store relevant test results, as described below infurther detail. Also, at step 105, the requisite load analysisparameters are downloaded for use during subsequent calculations. Theload analysis parameters include various threshold values used toanalyze or screen the Laplacian masks, linear filtering, motion blur,and the like.

[0071] The KGP is estimated in three distinct phases: a screening phase,analysis phase, and prediction and calibration phase. The ScreeningPhase and Analysis Phase are illustrated in FIG. 1. Basically, theScreening Phase commences at step 135 after the image has been selectedat step 130. During the Screening Phase, the image is processed tocalculate a mean image intensity and motion blur parameter. The imageenters the Analysis Phase at step 150 to measure certain parameters usedto calculate the hemoglobin and hematocrit, namely background curvature,area-to-perimeter ratio, vessel diameter and optical density. The testresults are written to the output file at step 155 and the process isrepeated for the next image at step 160.

[0072] II. Screening Phase

[0073] The Screening Phase is used to evaluate the quality of thespectral images by calculating two parameters: mean image intensity andmotion blur. These parameters are used to select images whose parametersare within certain thresholds. These images will provide the bettersamples for measuring the blood characteristics. The mean imageintensity represents the average of the intensity of all pixels in thespectral image. The mean image intensity is compared to a thresholdvalue to determine if the image is too dark or too bright for analysis.

[0074] To calculate the motion blur parameters, the method of thepresent invention uses a Fourier Transform to estimate the amount ofinter-field motion in the images as an estimate of intra-field blur. Theimages with considerable motion blur exhibit higher energy in the highfrequency of the Fourier Transform. One embodiment of the generaloperational flow for detecting motion blur is illustrated as controlflow 400 in FIG. 4. For example, at step 410, every thirty-second columnof the image is sampled. At step 415, the intensity values in eachcolumn are appended in reverse order. At step 420, a Fourier Transformis used to calculate the magnitude for each column. Step 425 sums andaverages the five highest frequency bins of the Fourier Transform foreach column. If the average value is equal to or exceeds a specificmotion blur threshold, the image is deemed to have failed and theaverage value for the image is recorded as the image's motion blurparameter at step 440. If the average value does not exceed the motionblur threshold, the image is deemed to have passed and the average valueis recorded as its motion blur parameter at step 435. Referring to FIG.1, this binary result, i.e. motion blur parameter, is calculated at step135, and then control flow 400 proceeds to step 140. As described, FIG.4 represents only an exemplary embodiment for detecting motion blur. Aswould be apparent to one skilled in the relevant art(s), othermethodologies for detecting the presence of significant energy in thehigh frequency region can be implemented and are considered to be withinthe scope of the present invention.

[0075] The motion blur threshold is one of the several load analysisparameters selected at Step 105 in FIG. 1. The motion blur threshold isbased on various factors, such as the type of spectral imagingapparatus. For example, the motion blur threshold can be one of severalparameters used to calibrate the spectral imaging apparatus to improvethe accuracy of the measurements and calculations resulting from themeasurements.

[0076] After the screening parameters have been calculated to measurethe quality of the image, a dark image is subtracted from the spectralimage. At step 140, the dark image is used as a zero offset to normalizethe spectral image. Then, the method of the present invention analyzesthe normalized spectral image to evaluate the content of the image. Inone embodiment, all images are analyzed regardless of the screeningresults. The rejection of measurements taken from poor images isimplemented at the Calibration and Prediction Phase. In anotherembodiment, optimal screening thresholds are entered at step 105. Theimages that do not meet the screening thresholds are rejected and arenot analyzed during the Analysis Phase.

[0077] III. Analysis Phase

[0078] During the Analysis Phase, three attributes are evaluated:background curvature, vascular structure and geometric properties. FIG.2 illustrates an operational flow of one embodiment of the AnalysisPhase. More specifically, FIG. 2 is one embodiment of a more detaileddescription of process step 150 from FIG. 1. The background curvature iscalculated at step 210. The background curvature is used to identify andmeasure the effects of larger background blood vessels that cast shadowsand compromise the details of the smaller vessels located in theforeground. This shadowing effect causes “curvature” in the image whenit is observed in the intensity domain as a three dimensional map. Thisshadowing or curvature interferes with the reliability of the opticaldensity measurements in the smaller vessels. In other words, thebackground curvature can attribute to an underestimate of the opticaldensity which produces an underestimate of the hemoglobin of thesubject. Therefore, this information can be used to eliminate imageshaving background curvature that exceeds a specific threshold, asdiscussed below.

[0079] A more detailed illustration of one embodiment of the process forcalculating the background curvature is shown in FIG. 6. Moreparticularly, FIG. 6 illustrates one embodiment of step 210. Backgroundcurvature can be estimated by fitting a second order three dimensionalpolynomial surface model to the spectral image. The model is given by:

F(x,y)=a₀ +a ₁ x+a ₂ y+a ₃ x ² +a ₄ y ² +a ₅ xy  (Eqn. 3)

[0080] where x and y are the row and column coordinates of the spectralimage, and a₀, a₁, a₂, a₃, a₄ and a₅ are the constant parameters of themodel. The parameters a, a₁, a₂, a₃, a₄ and a₅ can be found by using aleast squares minimization between the image and the backgroundcurvature model, such as:

Min(Σ_(x,y)(Im(x,y)−(a ₀ +a ₁ x+a ₂ y+a ₃ x ² +a ₄ y ² +a ₅ xy))²)  (Eqn. 4)

[0081] The solution of the minimization can be obtained by first takingthe derivative of the above equation and setting them to zero and tocreate the following matrix: $\begin{matrix}{{\begin{bmatrix}{\sum\limits_{xy}^{\quad}1} & {\sum\limits^{\quad}x} & {\sum\limits^{\quad}y} & {\sum\limits^{\quad}x^{2}} & {\sum\limits^{\quad}y^{2}} & {\sum\limits^{\quad}{xy}} \\{\sum\limits^{\quad}x} & {\sum\limits^{\quad}x^{2}} & {\sum\limits^{\quad}{xy}} & {\sum\limits^{\quad}x^{3}} & {\sum\limits^{\quad}{xy}^{2}} & {\sum\limits^{\quad}{x^{2}y}} \\{\sum\limits^{\quad}y} & {\sum\limits^{\quad}{xy}} & {\sum\limits^{\quad}y^{2}} & {\sum\limits^{\quad}{x^{2}y}} & {\sum\limits^{\quad}y^{3}} & {\sum\limits^{\quad}{xy}^{2}} \\{\sum\limits^{\quad}x^{2}} & {\sum\limits^{\quad}x^{3}} & {\sum\limits^{\quad}{x^{2}y}} & {\sum\limits^{\quad}x^{4}} & {\sum\limits^{\quad}{x^{2}y^{2}}} & {\sum\limits^{\quad}{x^{3}y}} \\{\sum\limits^{\quad}y^{2}} & {\sum\limits^{\quad}{xy}^{2}} & {\sum\limits^{\quad}y^{2}} & {\sum\limits^{\quad}{x^{2}y^{2}}} & {\sum\limits^{\quad}y^{4}} & {\sum\limits^{\quad}{xy}^{3}} \\{\sum\limits^{\quad}{xy}} & {\sum\limits^{\quad}{x^{2}y}} & {\sum\limits^{\quad}{xy}^{2}} & {\sum\limits^{\quad}{x^{3}y}} & {\sum\limits^{\quad}{xy}^{3}} & {\sum\limits^{\quad}{x^{2}y^{2}}}\end{bmatrix}\begin{bmatrix}a_{0} \\a_{1} \\a_{2} \\a_{3} \\a_{4} \\a_{5}\end{bmatrix}} = \begin{bmatrix}{\sum\limits_{xy}^{\quad}F} \\{\sum\limits_{xy}^{\quad}{xF}} \\{\sum\limits_{\quad}^{\quad}{yF}} \\{\sum\limits^{\quad}{x^{2}F}} \\{\sum\limits^{\quad}{y^{2}F}} \\{\sum\limits^{\quad}{xyF}}\end{bmatrix}} & \left( {{Eqn}.\quad 5} \right)\end{matrix}$

[0082] The above matrix of equations can be solved for a₀, a₁, a₂, a₃,a₄ and a₅ to obtain the model's parameters. The parameter a₀ gives theoverall constant or uniform average level of the image. The parametersa₁, a₂ and a₅ are a measure of the tilt of the background. Theparameters a₃ and a₄ are a measure of the curvature of the image.Parameters a₃ and a₄ are used to measure curvature of the background andscreen out the images that have high background curvature. For example,the following function:

Max {a ₃ , a ₄}  (Eqn. 6)

[0083] can be used as a thresholding criterion to screen out images withhigh background curvature. Other functional combinations of theparameters a₀, a₁, a₂, a₃, a₄ and a₅ can be used to evaluate otherproperties of the spectral image.

[0084] Referring to FIG. 6, matrix equation 5 is calculated at step 610and solved at steps 615-630 to determine the background curvatureparameters a₀, a₁, a₂, a₃, a₄ and a₅ (also shown as p in FIG. 6). Theparameters are calculated for each image and compared to the backgroundthreshold.

[0085] The second part of the Analysis Phase detects and segments thevascular structure within the spectral image from the background area.This is accomplished by using a second order derivative filter referredto as a Laplacian of Gaussian Pyramid (LOG) process. Referring to FIG.2, this process is implemented at step 215. A more detailed control flowof one embodiment of this process is illustrated in FIG. 3. As shown insteps 306-348, the method of the present invention uses a Log Transformto generate vessel and background area maps (denoted herein as MapVeinImand MapBackIm, respectively). A Gaussian pyramid is built by blurringand sub-sampling the original image, I(x,y). At each level of thepyramid, a second derivative (I_(x)(x,y) and I_(y)(x,y)) is computed bylinear filtering. The Laplacian Image (I² _(x)(x,y) and I² _(y)(x,y)) iscomputed from the second derivative. The resulting Laplacian image willhave non-zero responses at the regions of vascularization and zeroresponses elsewhere. The Laplacian images are combined at each scale bycollapsing the Gaussian pyramid (i.e., blur, up-sample and add at eachlevel).

[0086] The combined images are thresholded to create a mask image,M(x,y) (also referred to herein as a vessel or vein mask, V_(Mask)).Values of one in the mask image correspond to regions ofvascularization, and values of zero correspond to non-vascular regions.In other words, referring to steps 355-370, vessel and background masks(V_(Mask) and B_(mask)) are generated by binarizing the Log image (i.e.,MapVeinIm and MapBackIm) using a fixed function of the mean of the Logimage, or:

V _(Mask) =M(x,y)=binarize(MapVeinIm,V _(Thresh))  (Eqn. 7)

B _(Mask)=(1−M(x,y))=binarize(MapBackIm, B _(Thresh))  (Eqn. 8)

[0087] where,

V _(Thresh)=1.5*V _(Mean)  (Eqn. 9)

B _(Thresh)=0.25*B _(Mean)  (Eqn. 10)

[0088] The vessel and background threshold parameters, 1.5 and 0.25,respectively, are calibration constants loaded at step 105 and depend onthe spectral imaging apparatus and other factors, such as the type ofimaging (e.g., sublingual or other tissues). In an embodiment, thethreshold parameters are experimentally determined numbers. In anotherembodiment, the threshold parameters are constants determined for thespecific instrument type and application (e.g., sublingual Hemoglobinmeasurement). V_(Mean) and B_(Mean) describe the vessel and backgroundmean energy, respectively, as discussed below.

[0089] Referring to FIG. 3, the process for generating the vessel andbackground masks begins at step 355, where the vessel and backgroundarea maps are masked with an aperture mask to remove dark corners. Forthe sake of brevity, the masked vessel and background area maps willalso be denoted herein as MapVeinIm and MapBackIm. At step 360, the meanenergy is determined for MapVeinIm and MapBackIm and is denoted asV_(Mean) and B_(mean). At step 365, the vessel and background thresholdsare determined as shown by equations 9 and 10. The vessel and backgroundmasks are calculated at step 370.

[0090] The binary values in the vessel and background masks are used toidentify blood vessels in the vascularized region(s). A vessel or veinimage, V(x,y), of this region can be created by computing theinner-product of the original intensity image with the mask image, or:

V(x,y)=I(x,y)* M(x,y)=I(x,y)* V _(Mask)  (Eqn. 11)

[0091] The non-vascular region represents the background structure. Abackground image, B(x,y), can be created by computing the inner-productof the original intensity image with the inverse of the mask image:

B(x,y)=I(x,y)*(1−M(x,y))=I(x,y)*B _(Mask)  (Eqn. 12)

[0092] Referring to FIG. 3, the background and vessel images arecomputed at step 370.

[0093] The method of the present invention also calculates a smoothvessel mask, referred to as a thin mask. Referring to FIG. 3, a mappedthin image (denoted herein as MapThinIm) is obtained at step 350 bysmoothing the MapVeinIm with an eleven tap hanning filter. The mappedthin image is thresholded at step 370 to calculate the thin mask. Thethin mask is based on the same vessel threshold (V_(Tresh)) used tocalculate the vessel mask (V_(Mask)). As discussed above, V_(Thresh) canbe experimentally determined or depend on the instrument and type ofapplication. As shown at step 225 in FIG. 2, once the thin mask iscalculated, a medial axis transformation is applied to the thin mask tofind the middle of the vessel segments. This is a one-pixel wide path,which shows the medial axis of the vessels and provides guidance onwhere to make subsequent measurements of the optical characteristics. Atstep 230, the thin mask is traced to find the nonzero pixels whichindicates the possible measurement locations.

[0094] Step 235 involves the generation of an edge gradient image,G_(Image). The edge gradient image is obtained by a two dimensionalconvolution of separable prefilter/derivative operators in two passes.The maximum value G. of the absolute edge gradient image is used. Themaximum value of the edge gradient image is masked to take edge regionsto generate a gradient mask, G_(Mask). The gradient mask is derived bybinarizing G_(MAX) at a mean threshold, or:

G _(Mask)=binarize(G _(MAX) , G _(Thresh))  (Eqn. 13)

[0095] where,

G _(Thresh)=2.0*mean(G _(MAX))  (Eqn. 14)

G _(MAX)=max(abs(G _(Image)))  (Eqn. 15)

[0096] The threshold value, 2.0, is loaded at step 105 and will varydepending on the spectral imaging apparatus and other factors. Thisprocess, as illustrated at step 235, improves the diameter estimationoccurring at step 240.

[0097] In the third part of the Analysis Phase, various geometricproperties are calculated and used to screen the spectral images andimprove the accuracy of the blood property measurements. These geometricproperties include a diameter and area-to-perimeter ratio for the bloodvessel of interest. First, the vessel diameter, D, is calculated bygenerating a diameter mask and using an area based technique to estimatethe diameter.

[0098] Referring to FIG. 2, at step 240, a diameter mask is generated byadding (logical or) the gradient mask, G_(Mask), to the vessel mask,V_(Mask) This mask is used in calculating the diameter of the vessels asthe algorithm traces along the vessels and makes measurements. FIG. 5Aillustrates one embodiment for calculating a circular mask, circulararea and diameter predictor parameters used in estimating the vesseldiameter, D. FIG. 5B illustrates one embodiment for estimating thevessel diameter, D, from the circular mask, circular area and diameterpredictor parameters calculated from the process shown in FIG. 5A.

[0099] Referring back to FIG. 2, step 210, a section of the vessel isdefined by a binary mask (V_(Mask)). Referring to FIG. 5A, at step 510,a centerline is obtained by skeletonizing the diameter mask usingmorphological operators. A 2R×2R region of the vessel to be measured isextracted and masked with a circular binary pattern for radius R. This2R×2R square image is then masked with a circular binary pattern togenerate a circular mask. The circular mask would have a “1” inside thecircle of radius R and “0” outside of the circle. Measurements are madeby counting the number of nonzero entries remaining to calculate theoccupied area for the vessel, denoted as A_(v). The total possible areafor the circular mask is determined by:

A _(c) =πR ²  (Eqn. 16)

[0100] The function g is calculated at step 515 as the tangent of theratio of A_(v) to A_(c), or:

g=tan(A _(v) /A _(c))  (Eqn. 17)

[0101] At step 520, a diameter predictor is generated by fitting a tendegree polynomial to g versus the vessel diameter.

[0102] The diameter is estimated by the using an area based technique atstep 265. Referring to FIG. 5B, a more detailed control flow isillustrated of one embodiment for determining the diameter estimate. Atstep 560, the circular mask is multiplied by a diameter subimage togenerate a masked diameter subimage. For example, the subimage can be asub-window of 31×31 pixels. At step 565, the number of nonzero pixels,N, are counted in the masked diameter subimage, denoted as N/A_(c). Thediameter, D, is estimated at step 570 by evaluating the predictor (fromstep 520) with the argument N/A_(c).

[0103] Another geometric property measured during the Analysis Phase isthe vessel's area-to-perimeter ratio. This ratio is calculated at step245 in FIG. 2. Subjects having low hemoglobin possess a lower number ofred blood cells in their blood vessels. For these subjects, vessels arefilled with small clumps of red blood cells and a large plasma layerbetween them. Since the plasma layer is transparent, the vessel wallsare very difficult to identify. This is a major problem for thesegmentation process. The segmentation process uses the intensitychanges or the changes in intensity gradient to segment vessels from thebackground of the image. For low hemoglobin subjects, this process willsegment out clumps of red blood cells instead of the whole vessel. Todetect images and subjects suffering from the unfilled vessel effect,the area-to-perimeter ratio is calculated for the vessel mask, V_(Mask).

[0104] Referring to FIG. 9, a more detailed control flow is illustratedof one embodiment for calculating the area-to-perimeter ratio. At step905, the vessel mask, V_(Mask), is retrieved (from the output filecreate at step 375), and at step 910, the number of nonzero pixels aresummed to measure the area, A. At step 915, a perimeter mask isgenerated by removing the interior pixels in the vessel mask, therebyleaving only the perimeter pixels. The number of nonzero pixels in theperimeter mask are summed at step 920 to measure the perimeter, P. Thearea-to-perimeter ratio is calculated at step 925 as:

A/P=Area of the Mask÷Perimeter of the Mask  (Eqn. 18)

[0105] For low hemoglobin subjects, the vessel masks are very clumpy andthe area of the vessel mask is considerably smaller. The perimeter ofthe vessel mask is also much longer for low hemoglobin subjects than forsubjects with a higher hemoglobin. Thus, Equation 18 can be used toscreen out images having the unfilled vessel effect, as well asdetermine if the subject has a low hemoglobin concentration. If thesubject is determined to have low hemoglobin, the measurements for thesmall diameter vessels can be rejected during the Calibration andPrediction Phase. For example, the image can be discarded if thediameter estimate is less than 70 microns. The measurements from thelarger vessels, e.g. 70 micron or greater, would provide a betterestimate of the subjects hemoglobin.

[0106] As discussed above in relation to generating the thin mask andgradient mask, measurements are made at those locations exhibiting goodmeasurable properties. This is determined at steps 250-260 and steps275-280 in FIG. 2 by taking 31×31 pixel windows of the linear segment ofthe vessel with approximately similar gradient and background on bothsides and a background count higher than 300 and less than 1000.

[0107] Referring to FIG. 2, at steps 285-290, as each spectral image isprocessed during the Analysis Phase, the estimated diameter, D, and theoptical density (or contrast of the vessel), denoted as OD or K, aredetermined and stored in an output file (as also shown at step 155). Theoptical density is calculated from the mean of the vessel image V(x,y),denoted as I,, and the mean of the background image B(x,y), denoted asI_(b). For example, the optical density can be measured by:

[0108]K=log₁₀(I _(b) ÷I _(v))  (Eqn. 19)

[0109] As shown at step 250, the diameter and optical density arecalculated for each nonzero pixel in the thin mask. Afterwards, at step292, contrast value is normalized and averaged as:

L=K÷D  (Eqn. 20)

[0110] This value is also stored to the output file, at step 155, foruse in the Calibration and Prediction Phase.

[0111] IV. Calibration & Prediction Phase

[0112] The Calibration and Prediction Phase processes the valuesobtained from the Screening and Analysis Phases, namely the mean imageintensity, motion blur parameters, background curvature, optical densityor contrast, diameter and area-to-perimeter ratio. FIG. 7 illustratesone embodiment of a control flow for implementing the Calibration andPrediction Phase. At step 703, the recorded measurements are retrievedfrom the output file and, at step 705, spectral images having backgroundcurvature, motion blur, and mean intensity exceeding predeterminedthreshold values can be discarded, as discussed above, to select theimages having better quality. At step 710, the spectral image isscreened to detect low hemoglobin as discussed in reference to step 245.If low hemoglobin is detected, images having small diameter estimates,e.g. less than 70 microns, can be discarded.

[0113] At step 715, the normalized optical density, L, is taken for eachspectral image that passes the screening tests in steps 705 and 710.Step 715 also determines the average value of the normalized opticaldensity for all images from the subject.

[0114] The normalized optical density is used to measure the hemoglobinin the subject because L is functionally related to the hemoglobin andcan be expressed as:

Hb=aL÷b  (Eqn. 21)

[0115] where a and b are calibration constants that can be determinedempirically. For example, referring to steps 720 and 725, thecalibration constants can be determined by performing a least squaresfit between L and the actual or known Hb of the subject. Once theconstants are determined, the hemoglobin can be estimated by usingEquation 20 at step 730. As can be seen, equation 22 shows a linearrelationship between Hb and L. However, Hb can also be represented as anonlinear function of L.

[0116] At step 735, the NCCLSEP-9A protocol is used to measure theagreement between two analytic measurement methods for clinicalchemistry devices. In an embodiment, the protocol is used to calculatethe amount of agreement between the Hemoglobin measured at step 725 orstep 730 versus the standard Coulter method. Step 735 is optional andprovides a prediction function that gives the best estimate of theHemoglobin from the measurements.

[0117] In another embodiment, steps 720, 725 and 735 are omitted. Inthis embodiment, an optimal predictor function is implemented at step730 that is based on the instrument and data acquisition method. Hence,the calibration step (EP-9A calculations) will not be implemented, andthe measurements would only provide a prediction.

[0118] As is apparent from the foregoing description, the presentinvention was developed primarily to analyze blood components in anon-invasive manner. However, it will be clear to persons skilled in therelevant art(s) that the analysis techniques of this invention haveutility beyond the medical applications described above. The inventionhas application outside the medical area and can be used generally toanalyze visualizable components in a fluid flowing in any vascularsystem, such as a tube, the walls of which are transparent totransmitted and reflected light.

[0119] The present invention can be implemented using hardware, softwareor a combination thereof and can be implemented in one or more computersystems or other processing systems. In fact, in one embodiment, theinvention is directed toward one or more computer systems capable ofcarrying out the functionality described herein.

[0120] An exemplary screening, analyzing, and calibration and predictionmeans for use in the present invention is shown as computer system 800in FIG. 8. Computer system 800 includes one or more processors, such asprocessor 804. The processor 804 is connected to a communicationinfrastructure 806 (e.g., a communications bus, cross-overbar, ornetwork). Various software embodiments are described in terms of thisexemplary computer system. After reading this description, it willbecome apparent to a person skilled in the relevant art(s) how toimplement the invention using other computer systems and/or computerarchitectures.

[0121] Computer system 800 can include a display interface 802 thatforwards graphics, text, and other data from the communicationinfrastructure 806 (or from a frame buffer not shown) for display on thedisplay unit 830.

[0122] Computer system 800 also includes a main memory 808, preferablyrandom access memory (RAM), and can also include a secondary memory 810.The secondary memory 810 can include, for example, a hard disk drive 812and/or a removable storage drive 814, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 814 reads from and/or writes to a removable storage unit 818 in awell-known manner. Removable storage unit 818, represents a floppy disk,magnetic tape, optical disk, etc. which is read by and written toremovable storage drive 814. As will be appreciated, the removablestorage unit 818 includes a computer usable storage medium having storedtherein computer software and/or data.

[0123] In alternative embodiments, secondary memory 810 can includeother similar means for allowing computer programs or other instructionsto be loaded into computer system 800. Such means can include, forexample, a removable storage unit 822 and an interface 820. Examples ofsuch can include a program cartridge and cartridge interface (such asthat found in video game devices), a removable memory chip (such as anEPROM, or PROM) and associated socket, and other removable storage units822 and interfaces 820 which allow software and data to be transferredfrom the removable storage unit 822 to computer system 800.

[0124] Computer system 800 can also include a communications interface824. Communications interface 824 allows software and data to betransferred between computer system 800 and external devices. Examplesof communications interface 824 can include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface824 are in the form of signals 828 which can be electronic,electromagnetic, optical or other signals capable of being received bycommunications interface 824. These signals 828 are provided tocommunications interface 824 via a communications path (i.e., channel)826. This channel 826 carries signals 828 and can be implemented usingwire or cable, fiber optics, a phone line, a cellular phone link, an RFlink and other communications channels.

[0125] In this document, the terms “computer program medium” and“computer usable medium” are used to generally refer to media such asremovable storage drive 814, a hard disk installed in hard disk drive812, and signals 828. These computer program products are means forproviding software to computer system 800. The invention is directed tosuch computer program products.

[0126] Computer programs (also called computer control logic) are storedin main memory 808 and/or secondary memory 810. Computer programs canalso be received via communications interface 824. Such computerprograms, when executed, enable the computer system 800 to perform thefeatures of the present invention as discussed herein. In particular,the computer programs, when executed, enable the processor 804 toperform the features of the present invention. Accordingly, suchcomputer programs represent controllers of the computer system 800.

[0127] In an embodiment where the invention is implemented usingsoftware, the software can be stored in a computer program product andloaded into computer system 800 using removable storage drive 814, harddrive 812 or communications interface 824. The control logic (software),when executed by the processor 804, causes the processor 804 to performthe functions of the invention as described herein.

[0128] In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine so as to perform the functions described herein will beapparent to persons skilled in the relevant art(s).

[0129] In yet another embodiment, the invention is implemented using acombination of both hardware and software.

[0130] V. Conclusion

[0131] While various embodiments of the present invention have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It will be apparent to personsskilled in the relevant art(s) that various changes in form and detailcan be made therein without departing from the spirit and scope of theinvention. Thus, the present invention should not be limited by any ofthe above described exemplary embodiments.

What is claimed is:
 1. A method for analyzing visualizable components ina fluid flowing in a vascular system, the walls of which aresubstantially transparent to transmitted and reflected light, using alight transmitting device that is capable of transmitting light throughthe walls of a region of the vascular system into a sub-surface regionthereof, an image capturing device capable of capturing images reflectedfrom the sub-surface region of the vascular system illuminated by thelight transmitting device to create a spectral image, and a processingunit in conmmunication with the image capturing device, comprising thesteps of: (a) receiving in the processing unit a reflected spectralimage of the subsurface region captured by the image capturing device;(b) screening said reflected spectral image to determine whether atleast one of a plurality of properties of said reflected spectral imageexceeds a predetermined threshold; (c) analyzing said reflected spectralimage to measure a background curvature of the vascular system in theregion of the vascular system through which light is transmitted; (d)analyzing said reflected spectral image to segment the vascular andnon-vascular regions within said reflected spectral image; and (e)measuring an optical density of the vascular region to calculate acontrast value between the vascular and nonvascular regions within saidreflected spectral image.
 2. The method according to claim 1, whereinthe fluid comprises blood flowing in a blood vessel of a mammalianvascular system, and the visualizable components comprise bloodcomponents, including red blood cells, white blood cells, platelets,etc., and wherein: step (a) comprises receiving in the processing unit areflected spectral image of the blood components in the region of thevascular system through which the light is transmitted and reflected;and step (c) comprises analyzing said reflected spectral image tomeasure the background curvature of the region of the blood vesselthrough which light is transmitted.
 3. The method according to claim 1,wherein step (e) comprises the step of adding a calibration constant tosaid contrast value.
 4. The method according to claim 1, wherein step(d) comprises the steps of: (i) generating a vessel image to identifythe vascular regions of said reflected spectral image and a backgroundimage to identify the non-vascular regions of said reflected spectralimage; (ii) generating a vessel diameter estimate of said vessel image;and (iii) generating an area-to-perimeter ratio of the vascular regions.5. The method according to claim 4, further comprising the step ofcalculating a smooth vessel image to generate a thin image.
 6. Themethod according to claim 4, further comprising the steps of: (iv)binarizing a log transform of said reflected spectral image to generatea vessel mask and a background mask; (v) computing the inner-product ofthe intensity of said reflected spectral image with said vessel mask togenerate said vessel image; and (vi) computing the inner product of theintensity of said reflected spectral image with said background mask togenerate said background image.
 7. The method according to claim 6,further comprising the steps of: (vii) generating a gradient mask;(viii) adding said gradient mask to said vessel mask to generate adiameter mask; and (ix) generating said diameter estimate from saiddiameter mask.
 8. The method according to claim 4, further comprisingthe step of generating a binary circular mask, wherein said diameterestimate is a function of said binary circular mask.
 9. The methodaccording to claim 4, further comprising the steps of: (iv) generating avessel contrast from a mean vessel image and a mean background image;and (v) generating a normalized contrast value from said diameterestimate and said vessel contrast.
 10. The method according to claim 1,wherein said at least one of a plurality of properties is selected froma group consisting of a slope ratio, a mean image intensity, a motionblur, a background curvature and an area-to-perimeter ratio.
 11. Themethod according to claim 10, wherein said area-to-perimeter ratio isused to detect a subject having a low hemoglobin concentration.
 12. Anapparatus for analyzing visualizable components in a fluid flowing in avascular system, the walls of which are substantially transparent totransmitted and reflected light, comprising: a light transmitting devicefor transmitting light through the walls of a region of the vascularsystem into a sub-surface region thereof; an image capturing device forcapturing images reflected from the sub-surface region of the vascularsystem illuminated by said light transmitting device to create aspectral image; and a processing unit in communication with said imagecapturing device and comprising: receiving means for receiving areflected spectral image of the subsurface region captured by said imagecapturing device, screening means for screening said reflected spectralimage to determine whether at least one of a plurality of properties ofsaid reflected spectral image exceeds a predetermined threshold, firstanalyzing means for analyzing said reflected spectral image to measure abackground curvature of the vascular system in the region of thevascular system through which light is transmitted, second analyzingmeans for analyzing said reflected spectral image to segment thevascular and non-vascular regions within said reflected spectral image,and third analyzing means for measuring an optical density of thevascular region to calculate a contrast value between the vascular andnon-vascular regions within said reflected spectral image.
 13. Theapparatus according to claim 12, wherein the fluid comprises bloodflowing in a blood vessel of a mammalian vascular system, and thevisualizable components comprise blood components, including red bloodcells, white blood cells, platelets, etc., and wherein: said receivingmeans comprises means for receiving in the processing unit a reflectedspectral image of the blood components in the region of the vascularsystem through which the light is transmitted and reflected; and saidfirst analyzing means comprises means for analyzing said reflectedspectral image to measure the background curvature of the region of theblood vessel through which light is transmitted.
 14. The apparatusaccording to claim 12, wherein said third analyzing means comprisesmeans for adding a calibration constant to said contrast value.
 15. Theapparatus according to claim 12, wherein said second analyzing meanscomprises: first generating means for generating a vessel image toidentify the vascular regions of said reflected spectral image and abackground image to identify the non-vascular regions of said reflectedspectral image; second generating means for generating a vessel diameterestimate of said vessel image; and third generating means for generatingan area-to-perimeter ratio of the vascular regions.
 16. The apparatusaccording to claim 15, further comprising calculating means forcalculating a smooth vessel image to generate a thin image.
 17. Theapparatus according to claim 15, further comprising: fourthgenerating-means for binarizing a log transform of said image togenerate a vessel mask and a background mask; first computing means forcomputing the inner-product of the intensity of said reflected spectralimage with said vessel mask to generate said vessel image; and secondcomputing means for computing the inner product of the intensity of saidreflected spectral image with said background mask to generate saidbackground image.
 18. The apparatus according to claim 17, furthercomprising: fifth generating means for generating a gradient mask andadding said gradient mask to said vessel mask to generate a diametermask; and sixth generating means for generating said diameter estimatefrom said diameter mask.
 19. The apparatus according to claim 15,further comprising: fourth generating means for generating a binarycircular mask, wherein said diameter estimate is a function of saidbinary circular mask.
 20. The apparatus according to claim 15, furthercomprising: fourth generating means for generating a vessel contrastfrom a mean vessel image and a mean background image; and fifthgenerating means for generating a normalized contrast value from saiddiameter estimate and said vessel contrast.
 21. The apparatus accordingto claim 12, wherein said at least one of a plurality of properties isselected from a group consisting of a slope ratio, a mean imageintensity, a motion blur, a background curvature and anarea-to-perimeter ratio.
 22. The apparatus according to claim 21,wherein said area-to-perimeter ratio is used to detect a subject havinga low hemoglobin concentration.
 23. A computer program productcomprising a computer useable medium having computer program logicrecorded thereon for analyzing visualizable components in a fluidflowing in a vascular system, the walls of which are substantiallytransparent to transmitted and reflected light, using a lighttransmitting device that is capable of transmitting light through thewalls of a region of the vascular system into a sub-surface regionthereof, an image capturing device capable of capturing images reflectedfrom the sub-surface region of the vascular system illuminated by thelight transmitting device to create a spectral image, and a processingunit in communication with the image capturing device, said computerprogram logic comprising: first computer program product means forreceiving in the processing unit a reflected spectral image of thesubsurface region captured by the image capturing device; second programproduct means for screening said reflected spectral image to determinewhether at least one of a plurality of properties of said reflectedspectral image exceeds a predetermined threshold; third program productmeans for analyzing said reflected spectral image to measure abackground curvature of the vascular system in the region of thevascular system through which light is transmitted; fourth programproduct means for analyzing said reflected spectral image to segment thevascular and non-vascular regions within said reflected spectral image;and fifth program product means for measuring an optical density of thevascular region to calculate a contrast value between the vascular andnon-vascular regions within said reflected spectral image.
 24. Acomputer program product according to claim 23, wherein the fluidcomprises blood flowing in a blood vessel of a mammalian vascularsystem, and the visualizable components comprise blood components,including red blood cells, white blood cells, platelets, etc., andwherein: said first computer program product means comprises computerprogram product means for receiving in the processing unit a reflectedspectral image of the blood components in the region of the vascularsystem through which the light is transmitted and reflected; and saidthird computer program product means comprises computer program productmeans for analyzing said reflected spectral image to measure thebackground curvature of the region of the blood vessel through whichlight is transmitted.